Jul 28, 2010 - socialist countries to high values in the Scandinavian countries and East Asia. ... Even for simple produ
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Jappelli, Tullio
Working Paper
Economic literacy: An international comparison
CFS Working Paper, No. 2010/16 Provided in Cooperation with: Center for Financial Studies (CFS), Goethe University Frankfurt
Suggested Citation: Jappelli, Tullio (2010) : Economic literacy: An international comparison, CFS Working Paper, No. 2010/16, http://nbn-resolving.de/urn:nbn:de:hebis:30-78683
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No. 2010/16 Economic Literacy: An International Comparison
Tullio Jappelli
Center for Financial Studies Goethe-Universität Frankfurt House of Finance Grüneburgplatz 1 60323 Frankfurt Deutschland
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CFS Working Paper No. 2010/16
Economic Literacy: An International Comparison* Tullio Jappelli1 July 28, 2010
Abstract: Many studies show that most people are not financially literate and are unfamiliar with even the most basic economic concepts. However, the evidence on the determinants of economic literacy is scant. This paper uses international panel data on 55 countries from 1995 to 2008, merging indicators of economic literacy with a large set of macroeconomic and institutional variables. Results show that there is substantial heterogeneity of financial and economic competence across countries, and that human capital indicators (PISA test scores and college attendance) are positively correlated with economic literacy. Furthermore, inhabitants of countries with more generous social security systems are generally less literate, lending support to the hypothesis that the incentives to acquire economic literacy are related to the amount of resources available for private accumulation.
JEL Classification: E2, D8, G1
Keywords: Economic Literacy, Human Capital, Social Security
* I am grateful for helpful comments from an anonymous referee, Dimitris Christelis, Elsa Fornero, Luigi Guiso, Michalis Haliassos and participants at the SAVE Conference on Economic and Psychological Aspects of Households’ Saving Behaviour: Oldage Provision, Financial Literacy and the Financial Crisis (Mannheim, June 29-30, 2009), and to the Italian Ministry of Education for financial support. 1 University of Naples Federico II, CSEF, and CEPR
1. Introduction
Households have interacted with financial markets in the last 20 years, much more so than in the past, and also have been exposed to increased financial risk as a consequence of financial market liberalization and policy reforms aimed at promoting retirement savings through private pension funds and individual retirement accounts. Although to different extents, these trends are affecting all countries and all dimensions of economic transactions, from payment needs, as witnessed by the growth of the credit card industry, portfolio investments, and borrowing in the mortgage and consumer credit markets. Many of these activities, however, are entered into by uninformed individuals. The recent crisis has amplified the risks that people face when they lack the financial sophistication required to absorb financial shocks. Other things equal, differences in economic literacy create the potential for significant distributional consequences of a financial crisis, because unsophisticated investors are more exposed to financial market fluctuations then investors that are able to manage and diversify risks. The risks are especially severe for individuals whose pensions depend on stock market developments and for the elderly whose assets are based on decisions made in the past and whose margins for adjustment are smaller. Some recent financial economic studies have made considerable progress in measuring economic literacy. Economists have tended to measure literacy through a rough selfassessment of respondents’ financial sophistication; however, there is a second generation of studies based on detailed and more reliable questions on finance. These surveys have established convincingly that a large proportion of the adult population knows very little about finance and that many individuals are unfamiliar with even the most basic economic concepts, such as risk diversification, inflation, interest compounding, and mortgage and other debt instruments (Lusardi, 2008). There is also substantial evidence that economic literacy differs widely across households and tends to be rather limited in the less educated, poorer demographic groups. What makes this evidence even more worrying is that many people are not even aware of their ignorance. Although considerable progress has been made on measuring economic literacy, its determinants, the effectiveness of financial education and the consequences of financial literacy for households’ financial decisions are not well understood. This paper adopts an
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international comparative perspective, which involves merging indicators of economic literacy with a wide set of macroeconomic and institutional variables. The purpose of the analysis is to study the factors that are more likely to explain international differences in literacy using cross-country and time-variable indicators. To study cross-country differences in economic literacy, the ideal dataset would include an assessment of financial knowledge and skills, such as is provided by OECD-PISA for 15year olds for math or science. In the absence of such detailed (and expensive) data, we rely on the IMD World Competitiveness Yearbook (WCY), which compiles summary indicators of economic literacy for 1995 to 2008. The indicators are computed based on interviews with senior business leaders in 55 countries; the WCY aggregates their responses by country to provide an overall score for economic literacy. The data show that economic literacy varies substantially across countries, from the lowest scores in some Latin American and former socialist countries to high values in the Scandinavian countries and East Asia. Regression analysis indicates that PISA test scores and educational achievement are positively associated with economic literacy. On the other hand, countries with high mandated savings in the form of social security contributions and resulting more limited resources for private wealth accumulation, show lower levels of financial literacy. The results are robust to the presence of other macroeconomic and institutional variables and country-fixed effects. These findings are consistent with standard human capital models where households’ knowledge depends on cognitive abilities and the incentives to acquire information, which, in turn, are related directly to the size of financial markets (Delevande, Rohwedder, and Willis, 2008). The paper is organized as follows. Sections 2 and 3 respectively, discuss the importance of economic literacy and review the existing international evidence. Section 4 describes the data used in the paper, and Section 5 reports the cross-section and panel regressions. Section 6 concludes.
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2. Why is economic literacy important?
Economic literacy is increasingly important for households’ decisions about on how to invest wealth and how much to borrow in financial markets. Literacy also has far-reaching consequences for the stability of the overall economy.
2.1. The asset side On the asset side, economic literacy is important because financial products have become extremely complex. Even for simple products, such as savings accounts and government bonds, there are usually several options and several different contracts, which makes choice more difficult. Furthermore, due to financial market innovations and deregulation, since the end of the 1980s, the number of financial products that is available has increased considerably, with many new options in terms of investment in equities and bonds. In many countries, households are more exposed to financial risks as a consequence of greater stock market participation and policy shifts aimed at promoting retirement/pension arrangements through individual retirement accounts and private pension funds. Several empirical studies have found that lack of economic literacy is associated with poor risk diversification, inefficient portfolio allocations and low levels of savings. Banks and Oldfield (2007) look at numerical ability and other dimensions of cognitive function in a sample of older adults in England (the English Longitudinal Study of Ageing ) and find that numeracy levels are strongly correlated with measures of retirement saving and investment portfolios, understanding of pension arrangements, and perceived financial security. In subsequent work, Banks, O’Dea and Oldfield (2009) look at the extent to which differences in numeracy and broader cognitive ability predict subsequent trajectories for key economic outcomes such as wealth, retirement income and key dimensions of retirement expectations. Christelis, Jappelli, and Padula (2010) study the relation between cognitive abilities and stockholding based on the Survey of Health, Assets, Retirement, and Expectations (SHARE), and find that the propensity to invest directly and indirectly in stocks (through mutual funds and retirement accounts) is strongly associated with mathematical ability, verbal fluency, and recall skills. In a related paper, Ardle, Smith, and Willis (2009) find that numeracy, measured
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through the accuracy of responses to three simple mathematical questions, is a strong predictor of total wealth, financial wealth, and the fraction of wealth held in stocks. Smith, Ardle and Willis (2009) extend the evidence studying the relationship between household wealth and the cognitive status of both spouses. Alessie, Lusardi, and Van Rooij (2008) study the relation between financial sophistication and wealth relying on specific measures of financial literacy available in a special module of the Dutch DNB Household Survey. The module contains basic questions on the ability to perform simple calculations and to understand compound interest, inflation, and money illusion, and more advanced questions on stock market functioning, characteristics of stocks, mutual funds and bonds, equity premiums, and the benefits of diversification. They find that financial sophistication is associated with higher wealth, higher probability to invest in the stock market and higher propensity to plan for retirement.1 Guiso and Jappelli (2008) relate financial literacy to portfolio diversification by Italian investors. They use the 2007 Unicredit Customer Survey (UCS), which has detailed indicators of investors’ portfolio choice, financial literacy and demographic characteristics. Financial literacy is strongly correlated to the degree of portfolio diversification, even controlling for other socioeconomic characteristics and proxies for risk aversion. The authors compare objective measures of financial literacy obtained through specific questions on finance, with investors’ self-assessment of financial knowledge, and find only a weak relation between the two measures: 50 percent of those with poor financial literacy report above average confidence on financial matters, while 15 percent of investors who score well on literacy confess to knowing little about finance. In the context of developing countries, Cole, Sampson, and Zia (2009) analyze the relation between economic literacy and participation in formal financial markets. Using survey data on India and Indonesia, they show that financial literacy is a powerful predictor of demand for financial services. However, in a field experiment where randomly selected unbanked households were offered finance education, they find that, with the exception of completely uneducated and financially illiterate households, the program had no effect on the likelihood of opening a bank savings account. Hastings and Tejeda-Ashton (2008) use survey responses and the results of an experiment involving participants in Mexico’s privatized 1
The study also addresses the endogeneity between financial literacy and wealth. To account for the fact that wealth, portfolio management and planning activities independently exert an effect on financial literacy, the study uses economic education as an instrument for financial information.
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social security system, to examine how financial literacy impacts on workers’ choice behavior and how simplifying the information related to management fees may increase measures of price elasticity sensitivity among the financially illiterate. They find that how information is presented to workers can have a substantial impact on the optimal fees that firms can charge in the marketplace. One of the limitations of all the studies cited is that the incentive to become financially literate depends on the level of wealth and the portfolio allocation, which give rise to an endogeneity bias. Two papers that address this crucial issue provide conflicting results. Christiansen, Schröter Joensen, and Rangvid (2008) use a large register-based panel data set containing detailed information on Danish investors’ educational attainment as well as financial and socioeconomic variables. The authors show that stockholdings increases if individuals have completed an economic education program and if an economist moves into the household. To sort out the double causality between portfolio choice and the decision to become an economist, Christiansen, Schröter, and Rangvid use improved access to education due to a new university as an instrument for economic education. The instrumental variable estimates suggest that causation runs from economic education to stock market participation. The endogeneity issue is also addressed by Cole and Kartini Shastri (2009) who show that financial literacy education mandated by US state governments does not have an effect on financial market participation. They show that participation rates among those who graduated before it became compulsory (and therefore were not exposed to financial literacy education) are identical to the rate for those graduates who were exposed to this program.
2.2. The debt side On the debt side, borrowing in mortgage markets, ownership of credit cards, and consumer credit have increased in almost all OECD countries. To evaluate the information available on different loan possibilities, choose among different credit instruments, and identify predatory lending necessitate “a minimum level of financial literacy and skills to distinguish between products” (OECD, 2005, p. 65). The recent crisis shows that poor economic literacy can affect not only the choice of individual investors and borrowers, but can be an aggravating factor in a recession because household debt plays a central role in the balance sheets of banks and other financial intermediaries. Using cross-country data on
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household debt, and panel data on arrears, Jappelli, Pagano, and Di Maggio (2008) find that household indebtedness is associated with increased “financial fragility”, as measured by the sensitivity of household arrears and insolvencies to the amount of lending and to macroeconomic shocks. Despite its importance and potentially damaging consequences, the debt side of economic literacy has received less research attention than the asset side. Lusardi and Tufano (2009) analyze a national sample of Americans with respect to their debt literacy, financial experience, and judgment about the level of their indebtedness. They measure debt literacy through a set of questions testing the respondents’ knowledge of fundamental concepts related to debt, and find that there is illiteracy in all segments of the population, but especially women and the elderly. The paper finds a strong relationship between debt literacy and both financial experience and debt load, and finds also that individuals with lower levels of debt literacy tend to transact in high-cost ways (incurring fees and using high cost borrowing). This finding lends support to the claim that low levels of economic literacy have contributed to debt buildup, which, in some countries (e.g. the US and Germany), has been accompanied by an increased number of insolvencies and bankruptcies.
2.2. The macro side Economic literacy also contributes to the good workings of markets and policies. First, lack of financial literacy may create more favorable conditions for deceitful financial practices and unfair competition in financial markets, and be a serious impediment to effective financial intermediation. In contrast, as stressed at the 2006 Meeting of the G8 Finance Ministers, “well-informed and educated financial consumers lead to better financial markets where rogue products are forced from the marketplace and confidence is raised” (G8, 2006). When households are well informed, they can also discipline policy makers, so that “better-informed citizenry makes for better economic policy-making” (Mishkin, 2008), a point stressed by Bernanke, who argues that improving financial literacy is a way to restore confidence in the economy: the Federal Reserve's mission of conducting monetary policy and maintaining a stable financial system depends upon the participation and support of an educated public. As the Fed pursues the monetary policy objectives that have been set out by Congress (price stability, maximum employment, and moderate long-term interest rates) it is
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essential that the public understands our objectives and our actions. Educating the public about the reasoning behind our decisions helps build confidence in our economic system another critical factor in keeping our economy running smoothly. (Bernanke, 2006)
3. International evidence
Despite the importance of economic literacy for households’ decisions and the proper functioning of financial markets, the evidence on the importance of literacy and the effectiveness of financial education is focused primarily on the US.2 There are surveys of other regions, but they are not comparable in either focus or method. A recent OECD report lists surveys in 12 countries that provide one or more indicators of economic literacy (OECD, 2005). 3 These surveys rely on two approaches to measuring literacy. One is to test respondents on their knowledge and understanding of financial terms and their ability to apply financial concepts to particular situations: usually available for the US, Italy, Korea and the Netherlands. The other approach is to ask respondents to self-assess their financial understanding and ability to deal with financial matters: these indicators are available for the UK, Japan, Australia and some other countries. Outside the OECD, two recent surveys elicit measures of economic literacy in India and Indonesia (Cole, Sampson, and Zia, 2009). Although these surveys differ in terms of respondents targeted, the approach to measuring economic literacy, and the methodology, we can identify some common findings. First, many countries exhibit a rather low level of literacy; second, economic literacy is correlated with education (as measured by school or college attendance). 4 Also, where comparisons are possible, respondents generally report knowing more about financial matters
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Lusardi (2009) surveys the empirical evidence and finds mixed support for the effectiveness of these programs, partly because attendance at seminars is voluntary, and partly because it is difficult to disentangle the consequences of an increase in financial education from peer and community effects in raising savings (Duflo and Saez, 2003). Willis’s (2008) view of the potentially negative role of financial education stresses that for some consumers financial education programs increase confidence without improving ability, thus leading to worse decisions. 3 According to the OECD report, in only 5 countries (Australia, Japan, Korea, the US and the UK) is there detailed information on methodology, results, questions asked, and target groups. 4 Guiso and Jappelli (2005) document that in the 1995 and 1998 Bank of Italy Surveys of Household Income and Wealth (SHIW) a significant proportion of households was not aware even of the existence of many financial instruments. The paper also explores the determinants of awareness, and finds that the probability that survey respondents are aware of the existence of stocks, mutual funds, and investment accounts is positively correlated with education, household resources, long-term banking relations, and proxies for social interaction.
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than is actually the case. Finally, surveys show that economic literacy tends to be associated with higher income and wealth. However, this should not be interpreted as a causal link running from literacy to wealth, because the incentives to learn about finance are directly related to the level of resources. Indeed, as mentioned in Section 2, sorting out the causality between economic literacy and portfolio outcomes is a major challenge. In principle, to enable cross-country comparison, a single questionnaire should be administered to a random sample of the population in each country and the data integrated with economic, demographic, and institutional variables. However, such an approach (like the OECD-PISA test of educational achievement among 15-year olds) would require substantial resources and coordination efforts. Christelis, Jappelli, and Padula (2010) make an attempt in this direction by analyzing indicators of cognitive abilities (including some related to economic literacy) in the 11 countries covered by SHARE. Their analysis of a sample of respondents aged 50 and over, shows that cognitive abilities tend to be higher in Northern Europe, decline with age, and be positively associated with a college education.5 However, there is wide variation of cognitive abilities within each country, age, and education group. Given the small number of countries they cover, explaining cross-country differences using SHARE data is clearly not feasible. In the absence of international microeconomic data, the present paper relies on a survey of business leaders, which data have so far not been used to study economic literacy or its determinants. There are two advantages to using this dataset. First, it provides consistent international comparison of economic literacy in 55 countries over the 1995-2008 period, allowing to relate literacy to macroeconomic and institutional variables within a panel framework. Second, experts with an international dimension are less subject to the fact that individuals in different countries might use different response scales. The limitations to these data are that they are only available in aggregate form, which does not allow analysis of specific socioeconomic groups.
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The cross-country variability of cognitive skills among people with similar levels of education is not a unique feature of SHARE data. For instance, the OECD-PISA survey finds a significant North-South gradient in mathematics, science and verbal skills among young (under 15 years) Europeans at the same school grade
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4. Indicator of economic literacy
Since 1995, the IMD WCY has published an indicator of economic literacy. The indicator is computed from a survey of senior business leaders who represent a cross-section of the business community in the countries examined, and merged with data drawn from international organizations. The sample distribution reflects a breakdown of industry by sectors (manufacturing, services, and primary), and the sample size is proportional to each country’s GDP. The survey questions are targeted to top and middle managers, nationals or expatriates, located in local and foreign enterprises in the country in question, who generally have an international experience and outlook. The surveys are administered in January for completion and return by March of the same year. The overall size of the survey is about 4,000 business leaders, and 55 countries. The economic literacy question asks respondents to evaluate, on a 0-10 scale, the sentence: “Economic literacy among the population is generally high”. Dropping missing values for some countries, we constructed a panel of 55 countries for 1995 to 2008: 14 in Asia, 7 in Latin America, 15 in the EU, 12 former socialist countries, and 7 other countries (South Africa, US, New Zealand, Norway, Canada, Switzerland, Australia). The survey also includes an “Education in finance” question (available only from 1999 to 2008), which asks for an evaluation of the statement: “Education in finance does meet the needs of the business economy”. Clearly the economic literacy indicator is more closely related to the level of literacy of the population at large. However, the correlation coefficient for the two indicators is 0.81, and the main results of the paper are unaffected if we use the indicator for education in finance.6 Figure 1 plots the distribution of economic literacy for the world, and highlights large international differences from a score of less than 3 for South Africa, Venezuela, Peru, Mexico, and Croatia, to values above 7 for Ireland, Finland, and Singapore. Most of the former socialist countries show low literacy scores. This points to an interpretation that the history of financial market developments matters, and that a relatively low development of stock and credit markets is associated with a low level of literacy.
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Table 5 reports fixed effects estimates using this alternative indicator of literacy.
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One caveat related to using the WCY index is that it indirectly elicits the level of economic literacy in each country based on managers’ and country experts’ responses, rather than a standardized survey of individuals. The only dataset that has some comparability with WCY is SHARE, which provides detailed information on cognitive abilities (including a few questions related to economic literacy) at the individual level, for 11 European countries.7 In SHARE the variable closest to economic literacy measures the ability to perform basic numerical operations and understanding of basic financial principles. Specifically, SHARE respondents are asked: (1) to find 10 percent of a number; (2) to compute the cost of a good that sells at half price; (3) to compute the cost of a new car based on knowing the cost of a used car and that the used car is two-thirds of a new car; (4) to find the value of an account balance after two years of an annual interest rate of 10 percent. On the basis of these questions, Dewey and Prince (2005) construct an indicator based on these questions. The indicator ranges from 1 to 5 and is a function of the number of questions answered correctly; its construction and the actual questions are provided in the Appendix to this paper. Although the SHARE variable is not the ideal measure of economic literacy because it includes only a few economic concepts, there is evidence that knowledge about numerical problems is related to financial outcomes. Ardle, Smith, and Willis (2009) suggest that more numerate individuals are more adept at complex decision-making including financial decisions, and also appear to be more patient and thus more likely to have saved and invested in the past. Examining the results from a 25-item test of financial knowledge in the Cognitive Economics Survey, Delevande, Rohwedder, and Willis (2008) find that the number series score has a strong and significant effect on the financial test score - as does educational attainment and number of economics courses the respondent has attended. In the context of the present study, the SHARE numeracy variable is quite useful because it is available for 11 countries for which IMD data are also available. Figure 2 plots the WCY and SHARE indicators, showing that the two series are strongly positively correlated (correlation coefficient is 0.79). In both surveys, Italy and Spain have the lowest scores, and Sweden, Switzerland, and Denmark the highest. Despite the very different survey design, countries are well aligned, which increases confidence in the WCY literacy indicator being a reasonable proxy for economic literacy. The comparison is useful also because the 7
It should be noted that SHARE is a representative of the population aged 50+, and not of the population at large.
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scale of the WCY indicator is not directly interpretable. Figure 2 shows that two points change in the WCY indicator (the distance between Italy and Belgium, or between France and Sweden) is associated with a one point change in the SHARE numeracy indicator. It would be rather arbitrary, however, to interpret the WCY indicator as a function of the number of correctly asked questions in each country, as in SHARE. Therefore, in the regressions analysis we shall standardize the WCY indicator and the independent variables to have mean zero and a standard deviation of one. We can make some comparisons between WCY and other measures of economic literacy also using the Cole, Sampson, and Zia (2009) survey responses. In Indonesia and India two survey measures of economic literacy are obtained through the responses to three questions adapted from Lusardi and Mitchell (2007), which makes the comparable with the US.8 Measured economic literacy in India and Indonesia is substantially lower than in the US, which is in line with WCY ranking.
5. Descriptive analysis
The most natural framework to study the determinants of economic literacy is to consider that people accumulate financial knowledge combining ability and effort according to a human capital production function similar to Cunha and Heckman (2007). Applying this framework to the context of economic literacy, Delevande, Rohwedder, and Willis (2008) and Willis (2009) suggest that the incentives to acquire financial knowledge depend on the level of private resources: while increased knowledge raises the expected return from each dollar invested, the total value of the investment depends on the number of dollars to which the improved return is applied. Thus, incentives to acquire financial knowledge are greater for individuals with higher levels of resources available for investment.9 Similarly, investment 8
The questions are: (1) Suppose you borrow 100,000 rupiahs from a money lender at an interest rate of 2% per month, with no repayment for 3 three months. After 3 months, do you owe less than 102,000 rupiahs, exactly 102,000 rupiahs, or more than 102,000 rupiahs? (2) If you have 100,000 rupiahs in a savings account earning 1% interest per annum, and prices for goods and services rise 2% over a 1-year period, using the money in the account, can you buy more than, less than, or the same amount of goods in 1 year as you could today? (3) Is it riskier to plant multiple crops or one crop? 9 Van de Berg et al (2009), using a Dutch longitudinal database, find that cognitive functioning of elderly individuals may be affected by negative economic shocks such as job loss or the reduction of pension benefits, and by events such as the loss of a child or partner or the onset of a serious chronic condition.
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will be greater among people with lower costs or greater efficiency in acquiring additional knowledge because of their greater ability or because of their greater financial knowledge obtained through formal education. To apply this framework to our cross-country data, we relate economic literacy to measures of human capital, social interactions, and resources. Similar to cognitive abilities, we use the PISA test scores (available for 1995, 2000, 2003 and 2006 for a maximum of 44 countries)10 and formal education, measured by college enrolment rates and health conditions (proxied by life expectancy).11 Countries differ also in the opportunities to exploit cognitive abilities. We thus consider technological infrastructures (internet connections or computers per capita) and social interactions (proxied by the fraction of urban population) to measure how abilities can be combined to obtain additional financial knowledge.12 Finally, to proxy for the resources available for financial investments we use the generosity of the social security system (measured by the social security contributions rate), GDP per capita (PPP adjusted), and an indicator of financial development (the GDP ratio of stock market capitalization and private credit). In the cross-section analysis, each of these variables is averaged over the 1995-2008 period, and merged with institutional and legal indicators available in the World Bank Doing Business dataset (degree of contract enforcement, judicial efficiency, legal origin of the country, quality of credit information sharing). Statistics for the 1995-2008 sample are reported in Table 1. The most informative and reliable indicator of cognitive ability is provided by the PISA scores (Hanushek and Woessman, 2008). Figure 3 shows that there is a strong positive association between economic and mathematical abilities, and that the effects are potentially large. For example, in countries where the PISA score is less than 400, the indicator of economic literacy does not exceed 4, while in almost all countries with math scores above 500 the indicator for economic literacy is higher than 6. There is also a positive correlation between economic literacy and the fraction of the adult population with college education: in
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PISA is available for 27 countries in 1998, 28 in 2000, 33 in 2003 and 44 in 2006. For each country, we take the average value where more than one observation is available. 11 Ardle, Smith, and Willis (2009) mention that factors associated with lower cognitive performance include low socioeconomic status, birth complications, and poor early nutrition. 12 As noted in a recent OECD report, access through internet to many financial products has reduced transaction costs, but also increased the likelihood that consumers will encounter sophisticated financial assets (OECD, 2005).
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countries with college achievement rates below 25 percent economic literacy is less than 5 and that where college achievement exceeds 40 percent, literacy is above 6. The share of the urban population is also positively correlated to literacy (see Figure 4). Countries where most of the population is concentrated in large cities (e.g. Australia, Belgium, Hong Kong, Israel) also feature relatively high literacy, lending support to the idea that more intense social interactions are associated with higher literacy.13 Economic literacy is positively correlated to economic development (measured by GDP per capita) and financial development (the GDP ratio of stock market capitalization plus private credit, the most widely used indicator of financial development (Beck, DemirgüçKunt, and Levine, 2009). These correlations may be interpreted as financial development raising the volume of saving and the incentives to learn about finance. However, such a conclusion would be premature, because the two-way correlation may be driven simply by financial development being correlated to education, GDP per capita and other determinants of literacy. Furthermore, economic literacy can affect financial development, for at least three reasons. First, higher literacy leads to more efficient allocation of savings and higher per dollar returns, attracting more investment and growth in the country. Second, higher literacy might induce greater stock market participation and financial market depth, as shown in Christelis, Jappelli, and Padula (2010) and Alessie, Lusardi and Van Rooij, (2007). Third, as Bernanke (2006) claims, economic literacy might build confidence in the market economy, discipline financial intermediaries, and create a better policy environment for growth. The social security payroll tax rate is directly related to the amount of mandated saving in the form of social security contributions and, therefore, is also an indicator of the resources available for private accumulation, particularly in the form of private pension funds, life insurance, and other retirement savings vehicles. Since payroll tax is set by the government, it can be fairly safely assumed that it is not affected by the level of economic literacy. This rules out that governments decide to introduce less generous socials security benefits in countries where people do not have sufficient economic literacy to manage their own pension investment well. This assumption is reasonable, as recent social security reforms (in Sweden,
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Hong, Kubik, and Stein (2004) show that stock market participation is higher among more socially connected individuals. A related line of research points out that trust is an important determinant of economic exchange and financial transactions. Guiso, Sapienza, and Zingales (2004) find that, other things being equal, the proportion of stockholders is higher in Italian provinces with relatively high social trust. People who are more active socially might be more inclined to trust, making the effects of sociability and trust difficult to distinguish empirically.
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Italy, Germany and elsewhere) were implemented to address population aging and fiscal crisis, rather than to raise the level of economic literacy of the country. Figure 5 shows that contribution rates are negatively correlated to economic literacy: countries where contribution rates are lower then 20 percent (Chile and New Zealand) score above 6 for economic literacy, while countries with higher contribution rates (Greece, Spain and Portugal) show relatively low literacy levels. Since the contribution rate is more likely to be exogenous with respect to economic literacy, in the literacy regressions we rely primarily on contribution rate to proxy for private resources. However, we also provide OLS and instrumental variables estimates based on financial development and GDP per capita.
6. Regression analysis
Table 2 shows the sample average of the variables of interest for each country and reports a first set of regressions for the 46 countries with non-missing observations. The baseline specification includes the math score in PISA, the social security contribution rate, and the share of urban population, which arguably are exogenous variables. To ease the interpretation of the results, in the regression analysis each of the variables is standardized to have mean zero and a standard deviation of one. The coefficients of each of the three variables are precisely estimated, and have the expected sign. The math score and the share of urban population are positively associated with literacy, while the coefficient of the social security tax rate is negative, lending support to the hypothesis that incentives to acquire economic literacy are higher when savings mandated by government are lower. In terms of economic significance, an increase of one standard deviation in math score (equivalent to moving from Ireland to Korea) is associated to an increase in literacy of 0.54 standard deviations. Likewise, an increase of one standard deviation of urbanization (equivalent to moving from Finland to France) is associated to an increase of 0.26 standard deviations of literacy; and an increase in one standard deviation of the social security contribution rate (for instance, moving from Germany to Italy) is associated with a reduction of 0.31 standard deviations of literacy.
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Column 2 of Table 2 extends the baseline specification introducing a dummy for former socialist countries. The coefficient of this dummy is negative and statistically different at the 5 percent level, in line with the fact that for historical and institutional reasons the populations in these countries are less familiar with portfolio management and financial decisions. In particular, in former socialist countries literacy is 0.55 standard deviations lower than in other countries. The coefficients of other regional dummies (for Latin America, Asia and other European countries) are not statistically different from zero. The last two columns in Table 2 repeat the estimation using a robust estimation method to check for the effect of influential observations; the results are unaffected. In what follows, we provide a number of checks for the correlations in Table 2, expanding the variables used in the estimation, controlling for the potential endogeneity of some of the variables, and reporting fixed effects estimates at the level of individual countries. Table 3 presents expanded baseline regression that include the additional variables: science score in PISA, secondary school enrolment rate, college achievement, life expectancy, internet connections, log of per capita GDP and GDP growth. None of these coefficients is statistically different from zero at standard confidence levels. The results of the baseline estimates are unaffected, with the exception of the coefficient of the share of urban population which is less precisely estimated. We next report regression estimates for the relation between economic literacy and three proxies for financial market deepening: GDP ratio of stock market capitalization, GDP ratio of private credit, and the sum of these two items (financial development). Table 4 shows that, other things being equal, the measures of financial market deepening are not correlated with literacy. Given the potential endogeneity of financial market development with respect to literacy, in the last two columns of Table 4 we supplement the OLS estimates with instrumental variable regressions. We draw on the large literature on the legal and institutional determinants of financial development to obtain our instruments, and use dummies for the legal origin of the country and an index of the strength of investors’ protection (a combined indicator of transparency of transactions, director liability, and shareholders’ ability to sue officers and directors for misconduct).14 The Sargan test does not reject the hypothesis of valid instruments. The F-tests 14
The variables are drawn from the World Bank “Doing Business” database. La Porta, Lopez-de-Silane, Shleifer, and Vishny (1997) argue forcefully that legal origin of the country and investors’ protection are strong determinant of the depth of its financial markets. Djankov, McLiesh, and Shleifer (2007) and Brown, Jappelli,
15
of the exclusion of the instrument set in the first-stage regression were statistically significant at the 1% level (5% in case of private credit). Since such a diagnostic has limitations when there is more than one endogenous regressor (as in column 3), we compute the partial R squared (Shea, 1997).15 In both specifications, the instrumental variable estimates confirm lack of significance of the indicators of financial development; the lack of correlation is not affected by the particular set of instruments used.16 The cross-sectional estimates can be criticized for excluding too many country-level characteristics that potentially are related to economic literacy and for which the regressions in Table 3 do not control. To address this issue, we exploit the panel structure of the sample; Table 5 reports the fixed effects estimates. We drop the dummy for former socialist countries (which is absorbed by the country fixed effects) and to the baseline regression add a cyclical indicator (GDP growth rate) and - in column (2) - proxies for financial development. Since for many countries only one set of PISA scores is available, we replace them with the fraction of individuals with college education. Both regressions confirm a positive and significant association between share of urban population and economic literacy. The coefficient of the social security contribution rate is statistically different from zero (at the 5 percent level) only in the second specification. The magnitude of the coefficients is smaller than in the OLS regressions in Table 2: a one standard deviation increase in the contribution rate is associated with a reduction of 0.15 standard deviations of literacy. In the panel estimates the coefficients of the school attainment variable are not statistically different from zero. This is expected, given that the level of education is a slowly changing variable whose effect is hard to pinpoint in a relatively short panel.17 Finally, it should be noted that since the institutional determinants of financial market deepening are
and Pagano (2009) find that information sharing among lenders is associated with improved availability and lower costs of credit. 15 Shea’s partial R-squared is a test of the individual explanatory power of the instruments, accounting for correlation among the instruments. The results obtained indicate that there is enough separate variation in the instruments. 16 We also experiment with an index of Creditor Rights as a measure of creditor legal protection built using the methodology proposed by La Porta, Lopez-de-Silane, Shleifer, and Vishny (1997). Higher values of this index imply that secured lenders are better protected in the case that a borrower defaults. As a measure of actual creditor protection, we include the variable Time to Enforce Payment, which measures the (log of the) number of days it takes for a creditor to secure an outstanding payment through the courts if a debtor defaults. Finally, we include among the instruments the variable Information Sharing in Credit Markets because Djankov, McLiesh, and Shleifer (2007) and Brown, Jappelli, and Pagano (2009) find that information sharing among lenders is associated with improved availability and lower costs of credit. 17 In the panel estimates the coefficients of internet connection and life expectancy are never statistically different from zero.
16
constant over time or change only slowly, in the panel regressions there is not enough variability in the instruments to provide reliable IV estimates. In columns (3) and (4) of Table 5 we repeat the estimation using education in finance as an alternative indicator of economic literacy (recall that the variable is available only from 1999 to 2008). The estimates show that education in finance is negatively associated with the social security contribution rate, confirming the results in columns 1 and 2. Instead, the coefficients of urban population, college achievement and financial development are not statistically different from zero.
7. Conclusions Many surveys have shown that investors have poor financial literacy. These surveys are targeted at different population groups around the world, and elicit economic literacy in very different ways, from self-assessment to detailed questions aimed at understanding whether individuals are familiar with basic economic concepts, portfolio management, and specific financial products. The data used in this paper offer a comprehensive assessment of literacy across the world based on a survey of executives in 55 countries, in 1995-2008. The advantage of the dataset is strict international comparability, which allows economic literacy to be related to the quantity and quality of human capital, technological infrastructure, economic, and financial development. The drawback to it is that the survey respondents are a selected group of managers and country-experts, and that data are only available in aggregate form, preventing analysis of specific socioeconomic groups. The descriptive analysis shows that literacy varies quite substantially among countries, and the regression analysis shows that its level depends on educational achievement, social interactions (as proxied by the share of urban population), and mandated savings in the form of social security contributions. The contribution rate is used as an (inverse) proxy for financial market deepening to minimize the risk of reverse causation between financial literacy and financial development. The findings can be rationalized using a standard human capital model, where financial knowledge depends on cognitive ability, and incentives to accumulate knowledge are directly related to the level of household resources invested in financial markets, and particularly in pension funds.
17
The paper has two implications for policy. First, the international comparison suggests that economic literacy improves with the drivers of human capital and financial market reform, both of which change slowly over time. Second, social security reforms associated with financial market deepening (e.g., the creation of private pension funds), by raising the incentive to acquire financial knowledge, eventually will lead also to improvements in economic literacy.
18
References Alessie, Rob, Annamaria Lusardi, and Maarten Van Rooij (2007), “Financial Literacy and Stock Market Participation,” NBER Working Paper No. 13565. Alessie, Rob, Annamaria Lusardi, and Maarten Van Rooij (2008), “Financial Literacy, Retirement Planning, and Household Wealth”, mimeo, Dartmouth College. Ardle, John J., James P. Smith, and Robert Willis (2009), “Cognition and Economic Outcomes in the Health and Retirement Survey,” NBER Working Paper 15266. Banks, James and Zoe Oldfield (2007), “Understanding Pensions: Cognitive Function, Numeracy and Retirement Saving,” Fiscal Studies 28, 143-170. Banks, James, Cormac O’Dea and Zoe Oldfield (2009), “Cognitive Function, Numeracy and Retirement Saving Trajectories,” Institute for Fiscal Studies, memo. Beck, Thorsten, Asli Demirgüç-Kunt and Ross E. Levine (2009), “A New Database on Financial Development and Structure (updated May 2009),” World Bank Policy Research Working Paper, forthcoming. Bernanke, Ben (2006), “A Message from Chairman Bernanke,” Federal Reserve Bank of Dallas, July. Brown, Martin, Tullio Jappelli and Marco Pagano (2009), Information Sharing and Credit: Firm-Level Evidence from Transition Countries,” Journal of Financial Intermediation 18, 151-72. Christelis, Dimitris, Tullio Jappelli, and Mario Padula (2010), “Cognitive Abilities and Portfolio Choice”, European Economic Review 54, 18-39. Christiansen, Charlotte, Juanna Schröter Joensen, and Jesper Rangvid (2008), “Are Economists More Likely to Hold Stocks?” Review of Finance 12, 465-96. Cole, Shawn, and Gaury Kartini Shastri (2009), “Smart Money: The Effect of Education, Cognitive Ability and Financial Literacy on Financial Market Participation,” Harvard Business School Working Paper, No. 09-071. Cole, Shawn, Thomas Sampson, and Bilal Zia (2009), “Money or Knowledge? What drives the Demand for Financial Services in Developing Countries?” Harvard Business School Working Paper, No. 09-117. Cunha, Flavio, and James Heckman (2007), “The Technology of Skill Formation,” American Economic Review. Papers and Proceedings 97, 31-47, May
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Delevande, Adeline, Susan Rohwedder, and Robert J. Willis (2008), “Preparation for Retirement, Financial Literacy and Cognitive Resources,” Michigan Retirement Research Center Working Paper 2008-190. Dewey, Michael E., and Martin J. Prince (2005), “Cognitive Function,” in Health, Aging and Retirement in Europe: First Results from the Survey of Health, Aging and Retirement in Europe, A. Börsch-Supan, A. Brugiavini, H. Jürges, J. Mackenbach, J. Siegriest, and G. Weber, eds. Mannheim: Mannheim Research Institute for the Economics of Aging. Djankov, Simeon, Caralee McLiesh and Andrei Shleifer (2007), “Private Credit in 129 Countries,” Journal of Financial Economics 84, 299-329. Duflo, Esther, and Emmanuel Saez (2003), “The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence from a Randomized Experiment,” Quarterly Journal of Economics 118, 815–842. G8 (2006), “Improving Financial Literacy. Summary of Discussion,” Moscow, April 18-19, http://www.g8finance.ru/en/documents/index.php?id19=58. Guiso, Luigi, and Tullio Jappelli (2005), “Awareness and Stock Market Participation,” Review of Finance 9, 537-567. Guiso, Luigi and Tullio Jappelli (2008), “Financial Literacy and Portfolio Diversification”, CSEF Working Paper 212. Guiso, Luigi, Paola Sapienza, and Luigi Zingales (2004), “The Role of Social Capital in Financial Development,” American Economic Review 94, 526-556. Hanushek, Eric A., and Ludger Woessmann (2008), “The Role of Cognitive Skills in Economic Development,” Journal of Economic Literature 46, 607-68. Hastings, Justine S. and Lydia Tejeda-Ashton (2008), “Financial Literacy, Information, and Demand Elasticity: Survey and Experimental Evidence from Mexico”, NBER Working Paper n. 14538. Hong, Harrison G, Jeffrey D. Kubik, and Jeremy C. Stein, (2004), “Social Interaction and Stock Market Participation,” Journal of Finance 59, 137-63. Jappelli, Tullio, Marco Pagano, and Marco di Maggio (2008), “Households’ Indebtedness and Financial Fragility,” CSEF Working Paper No. 208. La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny (1997), “Legal Determinants of External Finance,” Journal of Finance, 52, 1131-1150. Lusardi, Annamaria (2008), “Household Saving Behavior: The Role of Literacy, Information and Financial Education Programs”, NBER Working Paper n. 13824.
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Lusardi, Annamaria, and Olivia S. Mitchell (2007), “Baby Boomers Retirement Security: The Role of Planning, Financial Literacy and Housing Wealth,” Journal of Monetary Economics 54, 205-224. Lusardi, Annamaria, and Peter Tufano (2009), “Debt Literacy, Financial Experiences, and Overindebtedness”, NBER Working Paper No. 14808. Mishkin, F. (2008), “The Importance of Economic Education and Financial Literacy”, Speech before the Federal Reserve Board at the Third National Summit on Economic and Financial Literacy, Washington, D.C. Organisation for Economic Co-Operation and Development (2005), Improving Financial Literacy: Analysis of Issues and Policies. Paris: OECD.ù Shea, John (1997), “Instrument Relevance in Multivariate Linear Models: A Simple Measure,” Review of Economics & Statistics 79, 348-52. Smith, James P., John J. McArdle and Robert Willis (2009), “Financial Decision Making and Cognition in a Family Context,” Rand Corporation, mimeo. van den Berg, Gerard J., Dorly J.H. Deeg, Maarten Lindeboom and France Portrait (2009), The Role of Early-Life Conditions in the Cognitive Decline due to Adverse Events Later in Life,” University Amsterdam, mimeo. Willis, Lauren E. (2008), “Against Financial Literacy Education,” Public Law and Legal Theory Research Paper Series, Research Paper No. 2008-10. Willis, Robert J. (2009), “Disentangling Cognitive Function and Financial Literacy: Implications for Financial Retirement Security Research,” paper presented at the Conference on Financial Literacy in Times of Turmoil and Retirement Insecurity. Brookings Institution, Washington D.C., March 20.
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Appendix
1. The World Competitiveness Yearbook (WCY) The IMD World Competitiveness Yearbook (WCY) is a comprehensive annual report on the competitiveness of nations available for 1995 to 2008. The WCY includes 329 variables on the following topics: • • • •
Economic Performance (82 variables), covering domestic economy, international trade, international investment, employment, and prices. Government Efficiency (70 variables), covering public finance, fiscal policy, institutional framework, business legislation, and societal framework. Business Efficiency (67 variables), covering productivity, labor markets, finance, management practices and attitudes, and values. Infrastructure (110 variables), covering basic infrastructure, technological infrastructure, scientific infrastructure, health and environment, and education.
The WCY uses different types of data to measure quantitative and qualitative issues separately. Statistical indicators are acquired from international, national and regional organizations, private institutions and a network of 54 partner institutes worldwide. These statistics are referred to in the WCY as Hard Data and include 245 variables. The other variables are drawn from the annual Executive Opinion Survey and are referred to in the WCY as Survey Data. The Executive Opinion Survey was designed to quantify issues that are not easily measured, for example management practices, labor relations, corruption, environmental concerns, and quality of life. The Executive Opinion Survey is sent to executives in top and middle management in all of the economies covered by the WCY. The sample of respondents covers a cross-section of the business community in each economic sector: primary, manufacturing, and services, based on their contribution to the GDP of the economy. The survey respondents are nationals or expatriates, located in local and foreign enterprises in a country and who, in general, have an international dimension. The surveys are sent in January and are returned in April of each year. From the last Opinion Survey, WCY indicators were based on 3,960 responses from 57 countries. The following variables are drawn from the WCY database: economic literacy, life expectancy, fraction of urban population, internet and computers per capita, GDP per capita.
2. The OECD-PISA survey The OECD Programme for International Student Assessment (PISA - www.pisa.oecd.org) is a regular survey of 15-year olds which assesses aspects of their preparedness for adult life. Mathematical Literacy: the capacity to identify, to understand, and to engage in mathematics and make well-founded judgments about the role of mathematics, needed in current and future private life, occupational life, social life with peers and relatives, and life as a constructive, concerned, and reflective citizen. Scientific Literacy: the capacity to use scientific knowledge, to identify questions and to draw evidence-based conclusions in order to understand and contribute to decisions about the natural world and the changes wrought on it by human activity
22
3. Macroeconomic variables Education: Secondary and tertiary enrolment rates (Source: OECD, Education at a Glance). Financial development: Stock market capitalization relative to GDP and private credit relative to GDP (Source: Beck, Demirgüç-Kunt, and Levine, 2009).
4. Institutional variables The following variables are drawn from the Doing Business database, available at the World Bank web site. Legal origin. Identifies the legal origin of the company law or commercial code of each country. Source: La Porta, Lopez-de-Silane, Schleifer and Vishny (1997). Investor protection: Measures the strength of minority shareholder protection against directors’ misuse of corporate assets for personal gain. The indicators distinguish among three dimensions of investor protection: transparency of related-party transactions (extent of disclosure index); liability for self-dealing (extent of director liability index); and shareholders’ ability to sue officers and directors for misconduct (ease of shareholder suit index). The data are from a survey of corporate lawyers and are based on securities regulations, company law, and court rules of evidence transparency of transactions, director liability index, and shareholders’ ability to sue officers and directors for misconduct Information Sharing Index. The depth of credit information index measures the scope, accessibility and quality of credit information available through either public or private credit registries. The index ranges from 0 to 6, with higher values indicating the availability of more credit information, from either a public registry or a private bureau. A score of 1 is assigned for each of the following 6 features of the public registry or the private credit bureau (or both): • • • • • •
positive and negative credit information data on firms and individuals data from retailers, trade creditors, utility companies, and financial institutions at least 2 years of historical data data on loans below 1% of per capita incomes borrowers have the right to access their data in the largest registry in the economy.
Enforcing contracts. Indicators on enforcing contracts measure the efficiency of the judicial system at resolving a commercial dispute. The data are built following a step-by-step evolution of commercial sale disputes in the local courts. Data are collected from codes of civil procedure and other court regulations and surveys completed by local litigation lawyers (and, in 25% of the countries, also by judges).The value of a claim equals 200% of the national per capita income. Time to collect the claim is recorded in calendar days, counted from the moment the plaintiff files the lawsuit in court until payment. Costs are recorded as a percentage of the claim.
23
4. Numeracy indicator in SHARE The (abridged) questions on numeracy are as follows. Possible answers are shown in a card while the interviewer is instructed not to read them out to the respondent: 1. If the chance of getting a disease is 10 per cent, how many people out of one thousand would be expected to get the disease? The possible answers are 100, 10, 90, 900 and another answer. 2. In a sale, a shop is selling all items at half price. Before the sale a sofa costs 300 euro. How much will it cost in the sale? The possible answers are 150, 600 and another answer. 3. A second hand car dealer is selling a car for 6,000 euro. This is two-thirds of what it costs new. How much did the car cost new? The possible answers are 9,000, 4,000, 8,000, 12,000, 18,000 and another answer. 4. Let’s say you have 2,000 euro in a saving account. The account earns ten per cent interest each year. How much would you have in the account at the end two years? The possible answers are 2,420, 2,020, 2,040, 2,100, 2,200, 2,400 and another answer. The numeracy indicator is a function of the number of questions answered correctly, and ranges from 1 to 5. If a person answers (1) correctly she is then asked (3) and if she answers correctly again she is asked (4). Answering (1) correctly results in a score of 3, answering (3) correctly but not (4) results in a score of 4 while answering (4) correctly results in a score of 5. On the other hand if she answers (1) incorrectly she is directed to (2). If she answers (2) correctly she gets a score of 2 while if she answers (2) incorrectly she gets a score of 1.
24
Table 1 Descriptive statistics
Economic literacy Education in finance Math score in PISA survey Science score in Pisa survey Secondary school enrolment rate (%) Tertiary education (%) Social security contribution rate (%) Share of urban population (%) Life expectancy Stock market capitalization (% of GDP) Private credit (% of GDP) Financial development (% of GDP) Log per capita GDP GDP Growth Old Europe Latin America Asia Former socialist countries Other countries English origin French origin Investor protection index
Mean
Median
4.87 5.66 481.94 486.09 83.04 26.31 19.30 70.60 74.35 65.25 75.61 142.58 9.20 0.07 0.27 0.13 0.24 0.24 0.13 0.22 0.33 10.58
4.73 5.81 495.88 497.62 87.83 26.62 17.48 70.01 75.67 44.97 74.99 138.49 9.28 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.33
Standard deviation 1.44 1.24 55.06 47.57 12.20 11.97 12.10 15.86 5.07 52.53 44.02 89.79 1.18 0.04 0.45 0.34 0.43 0.43 0.34 0.42 0.47 3.25
Minimum
Maximum
2.06 3.54 353.17 385.11 54.74 4.93 1.08 27.85 56.08 7.41 12.41 21.66 6.31 -0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.33
7.50 7.93 561.82 550.10 99.13 49.38 49.88 99.46 81.16 229.90 167.83 389.73 11.07 0.21 1.00 1.00 1.00 1.00 1.00 1.00 1.00 18.33
Note. Variables are averaged over the 1995-2008 period, except for education in finance, available from 1999 to 2008. The sample includes the following countries. Asia: China, Hong Kong, India, Indonesia, Israel, Japan, Jordan, Korea, Malaysia, Philippines, Singapore, Taiwan, Thailand, Turkey. Latin America: Argentina, Brazil, Chile, Colombia, Mexico, Peru, Venezuela; New Europe: Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Lithuania, Poland, Romania, Russia, Slovak Republic, Slovenia, Ukraine; Old Europe: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom; Other countries: Australia, Canada, New Zealand, Norway, South Africa, Switzerland, United States.
25
Table 2 Regressions for economic literacy – baseline estimates OLS
Math score in Pisa Social security contribution rate (%) Share of urban population (%)
(1)
(2)
(3)
(4)
0.541 (0.084)*** -0.312 (0.082)*** 0.260 (0.095)***
0.525 (0.089)*** -0.317 (0.087)*** 0.282 (0.101)***
0.116 (0.082)
0.562 (0.080)*** -0.209 (0.087)** 0.180 (0.095)* -0.551 (0.214)** 0.248 (0.093)**
0.136 (0.087)
0.551 (0.087)*** -0.222 (0.095)** 0.202 (0.104)* -0.524 (0.235)** 0.254 (0.102)**
46 0.65
46 0.70
46 0.63
46 0.66
Former socialist countries Constant
Observations R-squared
Robust regression
Note. The dependent variable is the index of economic literacy. Left-hand-side and right-hand-side variables (except for the dummy for former socialist countries) have been standardized to have mean zero and standard deviation equal to one. The robust estimation method is implemented using the rreg robust estimation command in Stata, which performs an initial OLS regression, calculates the Cook's distance, eliminates the gross outliers for which the Cook's distance exceeds 1, and then performs iterations based on Huber weights. Standard errors are reported in parenthesis. One star indicates significance at the 10 percent level, two stars at the 5 percent level, three stars at the 1 percent level.
26
Table 3 Regressions for economic literacy – additional variables
Math score in Pisa Social security contribution rate Share of urban population (%) Former socialist countries Science score in Pisa
(1)
(2)
(3)
(4)
(5)
0.656 (0.292)** -0.207 (0.088)** 0.182 (0.096)* -0.537 (0.221)** -0.098 (0.292)
0.527 (0.127)*** -0.182 (0.096)* 0.165 (0.100) -0.533 (0.218)**
0.545 (0.108)*** -0.211 (0.088)** 0.174 (0.099)* -0.521 (0.250)**
0.427 (0.133)*** -0.189 (0.088)** 0.150 (0.097) -0.507 (0.216)**
0.492 (0.136)*** -0.224 (0.091)** 0.160 (0.101) -0.369 (0.385)
Secondary school enrolment rate
-0.056 (0.163) 0.129 (0.111)
College achievement Life expectancy
0.038 (0.159)
Internet connections per 100 people
0.189 (0.149)
Log per capita GDP
0.244 (0.095)**
0.249 (0.095)**
0.234 (0.112)**
0.202 (0.099)**
0.113 (0.182) -0.040 (0.129) 0.184 (0.137)
46 0.70
46 0.71
46 0.70
46 0.71
46 0.70
GDP growth Constant
Observations R-squared
Note. The dependent variable is the index of economic literacy. Left-hand-side and right-hand-side variables (except for the dummy for former socialist countries) have been standardized to have mean zero and standard deviation equal to one. All regressions are estimated by OLS. Standard errors are reported in parenthesis. One star indicates significance at the 10 percent level, two stars at the 5 percent level, three stars at the 1 percent level.
27
Table 4 Regressions for economic literacy: the role of financial development OLS
Math score in Pisa Social security contribution rate Share of urban population (%) Stock market capitalization (% of GDP) Private credit (% of GDP)
(2)
(3)
(4)
0.554 (0.112)*** -0.255 (0.102)** 0.238 (0.101)** 0.205 (0.138) -0.061 (0.151)
0.507 (0.103)*** -0.234 (0.099)** 0.255 (0.099)**
0.649 (0.198)*** -0.318 (0.154)** 0.234 (0.106)** 0.299 (0.268) -0.273 (0.393)
0.512 (0.130)*** -0.238 (0.125)* 0.257 (0.102)**
Financial development (% of GDP) Constant
IV
(1)
0.174 (0.086)**
0.158 (0.125) 0.160 (0.084)*
42 0.68
42 0.67
Observations R-squared Sargan test (p-value) Shea’s partial R squared: stock market capitalization Shea’s partial R squared: private credit F-test of the exclusion of the instruments
0.199 (0.092)** 42 0.66 0.65 0.24 0.13
0.148 (0.227) 0.160 (0.079)** 42 0.67 0.94
4.22
Note. The dependent variable is the index of economic literacy. Left-hand-side and right-hand-side variables have been standardized to have mean zero and standard deviation equal to one. In the IV estimates the instruments are: a dummy for English origin, a dummy for French origin, and an index of the strength of investors’ protection (based on the average of transparency of transactions, director liability index, and shareholders’ ability to sue officers and directors for misconduct). Standard errors are reported in parenthesis. One star indicates significance at the 10 percent level, two stars at the 5 percent level, three stars at the 1 percent level.
28
Table 5 Regressions for economic literacy and education in finance. Fixed effects panel estimates
Economic literacy
College achievement Social security contribution rate Share of urban population (%) GDP growth
(1)
(2)
(3)
(4)
0.026 (0.062) -0.101 (0.068) 0.681 (0.310)** 0.134 (0.060)**
0.149 (0.091) -0.276 (0.147)* -0.076 (0.507) 0.232 (0.089)***
0.046 (0.065)
-0.018 (0.073) -0.148 (0.070)** 0.678 (0.313)** 0.112 (0.066)* 0.125 (0.067)* 0.077 (0.052)
0.121 (0.109)
0.064 (0.106) -0.414 (0.166)** -0.096 (0.510) 0.225 (0.099)** -0.000 (0.101) 0.212 (0.087)**
367 46 0.06
324 41 0.07
314 46 0.07
277 41 0.07
Financial development Constant
Observations Number of countries R-squared
Education in finance
Note. The table reports fixed effects panel estimates. The dependent variables are the index of economic literacy and the index of education in finance. Left-hand-side and right-hand-side variables have been standardized to have mean zero and standard deviation equal to one. Standard errors are reported in parenthesis. One star indicates significance at the 10 percent level, two stars at the 5 percent level, three stars at the 1 percent level.
29
Figure 1 Economic literacy around the world South Africa Venezuela Peru Mexico Croatia Russia Ukraine Romania Portugal Brazil Colombia China Indonesia Poland Italy Lithuania Bulgaria India Spain Thailand Slovenia Turkey France Greece Slovak Republic United Kingdom Hungary Argentina Czech Republic Philippines Jordan Chile Germany United States Luxembourg Belgium New Zealand Estonia Austria Malaysia Korea Norway Canada Sweden Taiwan Israel Netherlands Switzerland Japan Denmark Australia Hong Kong Ireland Finland Singapore 0
2
4
6
8
Economic literacy
7
Figure 2 Comparison between WCY and SHARE indicators of economic literacy Denmark
Switzerland
Economic literacy, WCY 4 5 6
Sweden Austria Belgium Germany
Greece France Spain
3
Italy
2.5
3
3.5 Numeracy indicator in SHARE
30
4
4.5
8
Figure 3 Economic literacy and math score in the PISA survey
Finland Hong Kong Australia Denmark Switzerland Japan Netherlands Israel Taiwan Sweden Canada Norway Korea Austria Estonia Zealand Belgium Luxembourg United States New
Economic literacy 4 6
Ireland
Jordan
Germany
Chile
Czech Republic
Argentina Turkey
Greece Thailand
Hungary Kingdom SlovakUnited Republic France Slovenia Spain
Bulgaria Italy Brazil
Indonesia Colombia
Lithuania Poland
Portugal Croatia
Romania
Russia
2
Mexico
350
400
450 Math score in Pisa test
500
550
8
Figure 4 Economic literacy and share of urban population
Economic literacy 4 6
Finland Ireland
Australia Hong Kong Denmark Japan Switzerland Netherlands Israel Taiwan Sweden Canada Norway Korea Austria Estonia New Zealand Belgium Luxembourg United States Germany Chile Jordan Czech Republic Argentina Hungary United Kingdom Slovak Republic Greece France Turkey Slovenia Spain Bulgaria Lithuania Italy Poland Indonesia Colombia Brazil Portugal Romania Russia Croatia Mexico
2
Thailand
20
40
60 Urban population (%)
31
80
100
8
Figure 5 Economic literacy and social security contribution rate
Singapore
Economic literacy 4 6
Finland Ireland Hong Kong Australia Denmark Switzerland Japan Netherlands IsraelTaiwan Sweden Canada Norway Korea Malaysia AustriaEstonia New ZealandUnitedLuxembourg Belgium States Germany Chile Jordan Philippines Czech Republic Argentina Hungary United Kingdom Slovak Republic Greece Turkey Slovenia Thailand Spain India Bulgaria Italy Lithuania Poland Indonesia China Colombia Brazil Portugal RomaniaUkraine Russia Croatia Mexico Peru
France
Venezuela
2
South Africa
0
10
20 30 Social security contribution rate
32
40
50
CFS Working Paper Series: No.
Author(s)
Title
2010/15
Bartholomäus Ende Marco Lutat
Trade-throughs in European Cross-traded Equities After Transaction Costs – Empirical Evidence for the EURO STOXX 50 –
2010/14
Terrence Hendershott Albert J. Menkveld
Price Pressures
2010/13
Sarah Draus
Does Inter-Market Competition Lead to Less Regulation?
2010/12
Kai-Oliver Maurer Carsten Schäfer
Analysis of Binary Trading Patterns in Xetra
2010/11
Annamaria Lusardi Olivia S. Mitchell
How Ordinary Consumers Make Complex Economic Decisions: Financial Literacy and Retirement Readiness
2010/10
Annamaria Lusardi Daniel Schneider Peter Tufano
The Economic Crisis and Medical Care Usage
2010/09
Annamaria Lusardi Olivia S. Mitchell Vilsa Curto
Financial Literacy among the Young: Evidence and Implications for Consumer Polic
2010/08
Volker Wieland Maik Wolters
The Diversity of Forecasts from Macroeconomic Models of the U.S. Economy
2010/07
Antje Brunner Jan Pieter Krahnen
Hold-Up in Multiple Banking: Evidence from SME Lending
Copies of working papers can be downloaded at http://www.ifk-cfs.de
